Study plans 2016-2017 - IMT6071 - 5 ECTS


None – however the course content will be complementary to the course "BehaviouralBiometrics". The course "MachineLearning&quotI is recommended as an accompanying module for this course; although some concepts of applied statistics and decision theory are revisited in this course, candidates will benefit from the more rigorous treatment of the subject matter in IMT4612 and iMT 4632.

Expected learning outcomes


  • The candidate possesses knowledge at the most advanced frontier in the field of biometrics.
  • The candidate has mastered academic theory and scientific methods in biometrics.
  • The candidate is capable of considering suitability and use of different methods and processes in research in the field of biometrics.
  • The candidate is capable of contributing to development of new knowledge, theories, methods, interpretations and forms of documentation in biometrics.


  • The candidate is capable of formulating problems, planning and completing research projects in biometrics.
  • The candidate is capable of doing research and development at a high international level.
  • The candidate is capable of handling complex academic tasks.
  • The candidate can challenge established knowledge and practice in biometrics. More specifically after the course, the candidate should have the following capabilities:
    • developed a systematic understanding of biometric systems and their capabilities
    • mastered multiple modality-specific feature extraction and have the ability to evaluate their suitability for given acquisition characteristics
    • developed in-depth insights into statistical methods and tools for biometrics and their performance evaluation
    • the ability to synthesize multi-modal analysis methods and solve score normalisation problems in fusion systems
    • the ability to appraise and differentiate threats to biometric reference data, judging and realizing adequate protection mechanisms accordingly
    • the ability to perform in-depth assessment of biometric component placement within a security system
    • demonstrated the ability to design and defend a biometric security system when provided with a threat scenario

General competence

  • The candidate is capable of identifying relevant – and possibly new - ethical problems and exercising research in biometrics with academic integrity.
  • The candidate is capable of managing complex interdisciplinary tasks and projects.
  • The candidate is capable of disseminating the results of research and development in biometrics through approved national and international publication channels.
  • The candidate is capable of taking part in debates in international forums within the field of biometrics.
  • The candidate is capable of considering the need for, taking initiative to and engaging in innovation in the field of biometrics. More specifically the candidate will have the competence to
    • demonstrate the ability to design a biometric system suitable for a given scenario
    • judge the relevance of ethical and privacy issues
    • investigate for a given scenario technical solutions and evaluate them in a critical analysis.
    • synthesize new ideas during evaluation phase
    • communicate with peers in the biometric community in terms of reviewing research topics
    • manage team work


  • Fingerprint recognition
  • Vein recognition
  • Face recognition specifically focused on three dimensional data
  • Iris recognition
  • Multimodal biometrics
  • Score Normalization
  • Attack mechanisms
  • Privacy Enhancing Technologies
  • Revocable biometric references

Teaching Methods


Teaching Methods (additional text)

Seminar with term paper presentation

Form(s) of Assessment


Form(s) of Assessment (additional text)

Candidates must provide a research report (term paper) on a topic that is chosen by the candidate in coordination with the lecturer. The term paper should preferably not focus on a survey of methods but rather address original research and be submitted to a scientific conference (e.g. NISK, BIOSIG)

Grading Scale


External/internal examiner

The research report: Internal examiner

Oral presentation: Two internal examiners

Re-sit examination

 The whole course must be repeated.

Examination support

Dictionaries allowed (No calculator)

Coursework Requirements


Teaching Materials

[1] LI , S . Z. , AND JAIN, A. K. , Eds. Handbook of Face Recognition. Springer, 2011.

[2] MALTONI , D. , MAIO, D. , JAIN, A. K. , AND PRABHAKAR , S . Handbook of Fingerprint Recognition. Springer, 2009.

[3] WAYMAN, J . , JAIN, A. , MALTONI , D. , AND MAI O, D. , Biometric Systems. Springer, 2005.

[4] JAIN, L.C. , HALICI, U. , HAYASHI, I. ; LEE, S.B., TSUTSUI, S. Intelligent Biometric Techniques in Fingerprint and Face Recognition. CRC Press, 1999.

[5] TUYLS, P., SKROIC, B., KEVENAAR, T.  Security with Noisy Data. Springer, 2007

Additional information